We investigate a few approaches that have been considered
in the simulation and modeling of networks describing cell behavior. By
simulation it is meant the direct problem of determining cell behavior when
given a graph (network) specifying the interaction among genes. By cell
behavior we mean determining the amount of byproducts (mRNA or protein)
that each gene generates with time as it interacts with other genes. We
refer to modeling as the inverse problem namely, inferring the network graph
when given the data describing the cell's behavior. The modeling problem
has acquired significant importance in view of the present high volume of
cell data available from micro-array experiments. The emphasis of the paper
is in using the constraint logic programming paradigm to describe the simulation
of cell behavior. In that paradigm the same program describes both a problem
and its inverse. Basically one defines multi-dimensional regions, transitions
(specifying how control is transferred from one region to the other), and
trajectories (sequences of transitions describing cell behavior). The paradigm
is applied to several approaches that have been proposed to study simulation
and modeling. Several logic programs have been developed to prototype those
approaches under the same proposed paradigm. They include considering Boolean
and discrete domains. In each case the potential of obtaining practical
solutions to the inverse problem are discussed. The proposed paradigm is
related to machine learning and to the synthesis of finite-state automata.